A method for detecting a melody of an audio signal, including: dividing the audio signal into a plurality of audio segments based on a beat, detecting a pitch frequency of each frame of audio sub-signal in each of the audio segments, and estimating a pitch value of each of the audio segments based on the pitch frequency; determining a pitch name corresponding to each of the audio segments based on a frequency range of the pitch value; acquiring a musical scale of the audio signal by estimating a tonality of the audio signal based on the pitch name of each of the audio segments; and determining a melody of the audio signal based on a frequency interval of the pitch value of each of the audio segments in the musical scale.

A platform or system is disclosed for performing musical analysis to detect musical properties in received live or pre-recorded audio data. The analysis can include a synchronous analysis for generating estimated one or more transitory musical properties and an asynchronous analysis for generating one or more aggregate musical properties which can be applied to the transitory musical properties to generate confirmed musical properties, which can be stored as metadata associated with an audio file. In some cases, live audio data can be received, recorded, dynamically analyzed to provide realtime metadata (e.g., to a display), then the realtime metadata can be analyzed to provide confirmed, updated, or validated metadata. In some cases, initial analysis (e.g., dynamic analysis) can determine chord estimates, usable in further analysis (e.g., offline analysis) to estimate a musical key, which can then be applied to the chord estimates to determine the most likely chord estimates and determine chord progressions.

A platform or system is disclosed for performing musical analysis to detect musical properties in received live or pre-recorded audio data. The analysis can include a synchronous analysis for generating estimated one or more transitory musical properties and an asynchronous analysis for generating one or more aggregate musical properties which can be applied to the transitory musical properties to generate confirmed musical properties, which can be stored as metadata associated with an audio file. In some cases, live audio data can be received, recorded, dynamically analyzed to provide realtime metadata (e.g., to a display), then the realtime metadata can be analyzed to provide confirmed, updated, or validated metadata. In some cases, initial analysis (e.g., dynamic analysis) can determine chord estimates, usable in further analysis (e.g., offline analysis) to estimate a musical key, which can then be applied to the chord estimates to determine the most likely chord estimates and determine chord progressions.

A music composition and training system includes: identifying a first chord of a chord progression. Then, for multiple iterations, the system includes selecting a chord of the chord progression, beginning with the identified first chord, and: identifying one or more potential subsequent chords in such a way that each of the potential subsequent chords provide a musical progression; applying weighted criteria to each of the potential subsequent chords; selecting a second chord based on the weighting while also providing variety and unpredictability; and combining the second chord into the chord progression. The system then uses the generated chord progression to generate or teach music.

Systems, including methods and apparatus, for generating audio effects based on accompaniment audio produced by live or pre-recorded accompaniment instruments, in combination with melody audio produced by a singer. Audible broadcast of the accompaniment audio may be delayed by a predetermined time, such as the time required to determine chord information contained in the accompaniment signal. As a result, audio effects that require the chord information may be substantially synchronized with the audible broadcast of the accompaniment audio. The present teachings may be especially suitable for use in karaoke systems, to correct and add sound effects to a singer's voice that sings along with a pre-recorded accompaniment track.

A method and system for analyzing patterns in the relationships of notes of an input piece of music. The method comprises generating a set of the most frequently occurring note pitches in ascending pitch order that matches an interval pattern, and detecting out-of-key pitches that lie outside of this interval pattern. One or more potential key sequence bifurcations are identified which represent a list of possible key sequences according to forwards and backwards analysis. By finding patterns of repetition in the chordal sequences that may be generated according to these key sequence bifurcations, a key sequence that allows the most frequently recurring chord sequences may be chosen. Chord sequences may be analyzed by using ghost chords, temporary harmonic structures that are created, updated and finalized over time according to a combination of essential and inessential note fragments. The method further comprises identifying non-harmony pitches according to the analyzed chord sequence.

The present document relates to methods and systems for music information retrieval (MIR). In particular, the present document relates to methods and systems for extracting a chroma vector from an audio signal. A method (900) for determining a chroma vector (100) for a block of samples of an audio signal (301) is described. The method (900) comprises receiving (901) a corresponding block of frequency coefficients derived from the block of samples of the audio signal (301) from a core encoder (412) of a spectral band replication based audio encoder (410) adapted to generate an encoded bitstream (305) of the audio signal (301) from the block of frequency coefficients; and determining (904) the chroma vector (100) for the block of samples of the audio signal (301) based on the received block of frequency coefficients.

A method for generating a musical score, a device, and a computer-readable storage medium. The method includes: obtaining target audio; generating a chromagram of the target audio corresponding to each pitch class, utilizing the chromagram to identify a chord of the target audio, and obtaining chord information; performing mode detection on the target audio, and obtaining original key information; performing rhythm detection on the target audio and obtaining the beats per minute; performing identification on a beat type of each audio frame of the target audio, and determining an audio time signature on the basis of a correspondence relationship between a beat type and a time signature; utilizing the chord information, the original key information, the beats per minute, and the audio time signature and performing musical score rendering, and obtaining a target musical score.

Disclosed are a spiral curve type music sheet in which different notes are displayed at different positions on a spiral curve based on the pitches of notes, and an apparatus and method for providing a spiral curve type music sheet. The apparatus for providing a spiral curve type music sheet may include a memory configured to store a spiral curve type music sheet in which different notes are displayed at different positions on a spiral curve based on the pitch of the note and note data, and a processor configured to determine the note symbol position related to the note data on the spiral curve in the spiral curve type music sheet based on the frequency of the note data.

A server system 500 is provided for receiving video clips having an associated audio/musical track for processing at the server system. The system comprises a beat tracking module for identifying beat time instants (t.sub.i) in the audio signal and a chord change estimation module for determining a chord change likelihood from chroma accent information in the audio signal at the beat time instants (t.sub.i). Further, first and second accent-based estimation modules are provided for determining respective first and second accent-based downbeat likelihood values from the audio signal at the beat time instants (t.sub.i) using respective different algorithms. A final stage of processing identifies downbeats occurring at beat time instants (t.sub.i) using a predefined score-based algorithm that takes as input numerical representations of chord change likelihood and the first and second accent-based downbeat likelihood values at the beat time instants (t.sub.i).